1. Richard (Rick) Jones SWFDP Training Workshop on Severe Weather Forecasting Bujumbura, Burundi, Nov 11-16, 2013

2. Overview Overview of the key ingredients of deep convection Key stability indices and precipitable water (PW) or total column water TCW PW is the prime driver of convection Forecast of deep convection and limits of predictability

3. Basic Ingredients Source of moisture best described by precipitable water (PW) Sustained PW plumes are often associated with prolonged and record/near record events related to convectively available potential energy Method to lift the air Topography – upslope Density boundaries fronts; sea-breeze fronts Instability -> maximize lift & sustain development

4. Classic Stability forecast Indices & Methods The K-index The Totals Totals Index The Lifted Index Showalter Stability index (more mid-latitude) CAPE & CIN

5. Based on 850 to 500 hPa lapse rate to identify convective and heavy-rain producing environments as it accounts for moisture. With contribution of 850 hPa dew point and 700 hPa dewpoint depression KI = (T850-T500)+Td850-(T700-Td700) KI = (T850-T500)+Td850-(DD700)

6. KI normally above ~28-38 KI good for potential areas of convection Related to heavier rainfall when have lift and moisture Limitations when terrain above 850 hPa Good in models and in soundings

7. Climate Prediction Centre Africa http://www.cpc.ncep.noaa.gov/products/african_desk /cpc_intl/africa/africa.shtml http://www.cpc.ncep.noaa.gov/products/african_desk /cpc_intl/africa/africa.shtml

8. KI NCEP

9. PW

10. Using the K-index Heavy rainfall  combination of instability and deep moisture Favors values of K-index upper 20s to mid 30s. Still need deep moisture and lift to get convective response Sample values tropical (Laing 2004)

11. Totals Totals Index (TTI) Stability relative to 850 hPa and 500 hPa 2 components: Cross Totals: CT = Td 850 – T 500 Vertical Totals: VT = T 850 - T 500 TTI = (Td 850 + T 850 ) – 2T 500 Focus on instability between 850 and 500 hPa with a component of moisture at 850 hPa

12. TT

13. Lifted Index (LI) Lifted index is based on a parcel reaching the Lifting condensation level (LCL) then adiabatically lifting it to 500 hPa. It is often surface or moist boundary layer based Accounts instability based on difference LI = T lift - T 500

14. LI Areas Low LI often have convection LI < 0 is a good starting point LI often related to CAPE and as we will see and CAPE often relates to Precipitable water

15. LI

16. Showalter Stability Index (SSI) SSI similar to Lifted index but based an 850 hPa parcel reaching the Lifting condensation level (LCL) then adiabatically lifting it to 500 hPa. It is often surface or moist boundary layer based Accounts for instability based on difference SSI = T lift - T 500

17. The Power of Convective Available Potential Energy (CAPE) On energy diagram Tephi or Skew-T area proportional to energy Integrated value and like LI a theoretical parcel is lifted adiabatically from the LCL until the equilibrium level is reached. CAPE: convective available potential energy Release of energy produces deep updrafts Greater the area stronger the updraft Can have fat or skinny CAPE

18. CIN: convective inhibition Another equal area value based on Tephi- or Skew-T diagram CIN is negative and represents stability and the lift must be able to over to overcome it unless it is diminished

19. CCl convective condensation level LCL lifting condensation level

20. LFC and LCL See LFC2

21. CAPE

22. CAPE ValueStability 0Stable 0-1000Marginally Unstable 1000-2500Moderately Unstable 2500-3500Very Unstable 3500 or greater Extremely Unstable

23. CAPE and updrafts CAPE may also be related to updraft velocity via the relation Wmax = sqrt(2*CAPE) For example a CAPE of 2500 J/kg, the maximum updraft velocity would be about 71 m/s!! In reality, water loading, entrainment, and other factors can reduce Wmax by as much as a factor of 2.

24. CAPE

25. Direct Link CAPE & LI NE US based study CAPE ~1200JKg-1 with LI <0

26. NOAA NWS forecast soundings

27. MOGREPS soundings

28. Other Parameters Associated with heavy rainfall and convection Precipitable water (PW) Main driver of convection related to rainfall and convection Equivalent potential temperature Vertical Velocity Estimated from CAPE for updraft speed Areas of ascent in Numerical guidance which could favor releasing instability

29. Precipitable water is the main driver Need to know when PW is abnormally high High PW source regions PW often a proxy for CAPE High CAPE is often co-located with high PW values Heavy rainfall almost always associated with PW plumes

30. Equivalent Potential temperature Also known as pseudo-equivalent potential temperature is attained by parcel is lifted to the LCL and taken up a pseudo-adiabat (moist) to the level where air dries out and then dry adiabatically to 1000 hPa (the reference pressure). Normally  e increases with height. Convection it decreases with height Can use  e In vertical for stability Horizontal for boundaries which could trigger convection

31. ee

32. NCEP  e Produced in Plan view 925, 850, 700, 500, 300 and 200 hPa Should show boundaries Low over high values could favor convection CAPE easier to find convective instability

33. NCEP  e

34. Skew-T NCEP GFS profiles at: http://www.cpc.ncep.noaa.go v/products/african_desk/cpc _intl/skewt/gfs_profiles.shtm l

35. Limitations Indices are computed with fixed levels 850 hPa surface may be under ground This impacts KI,TTI and need to modify to a level above terrain Indices Will vary from grid point to point Model resolution will impact The details And what we can predict  next slide

36. Limits of Predictability: Error Growth higher resolution models will resolve features EFS may filter out Errors doubling time in days! Mesoscale error growth is fast! Finer scale features Fast growing error modes Fine scale features have rapid error doubling rates

37. Orlanski (1975) Scales and Resolution Effective Grid Resolution 66 44 Notes 100 km600 km400 km Synoptic scale ~1000km Mesoscale-  200- 2000km 40 km240 km160 km Meso-  dd Meso-  20-200km 30 km180 km120 km Clearly meso-  20 km120 km80 km Low end meso-  10 km60 km40 km Low end meso-  4km24 km16 km meso-  Meso-  2-20 km

38. Salient Points EFS and global models are too course for features which can really impact the local and regional weather Smooth out terrain features Smooth out finer scale features Flooding and severe weather events are typically meso scale features Finer resolution models and Local Area Models, short range EFS (see next slide) play a valuable role in filling the gaps Diagnosis of stability can help in many cases

39. UK LAM

40. Review/summary Overview of the key ingredients of deep convection Key stability indices and precipitable water (PW) PW main driver Forecast deep convection and limits of predictability

41. References Convective Indices Laing, Arlene G. (2004) Cases of Heavy Precipitation and Flash Floods in the Caribbean during El Niño Winters. Journal of Hydrometeorology. Volume 5, Issue 4 (August 2004) pp. 577-594 N Ravi, U C Mohanty, O P Madan, R K Paliwal. (1999) Forecasting of thunderstorms in the pre-monsoon season at Delhi. Meteorological Applications 6:1, 29-38 UCAR Comet: https://www.meted.ucar.edu/index.phphttps://www.meted.ucar.edu/index.php